Confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine

Qiang Cheng, Jale Tezcan, Jie Cheng

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

We consider estimating the confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine (LS-SVM). Explicit formulas are derived for confidence and prediction intervals. The accuracy of the derived analytical equations is assessed by comparing with wild cluster bootstrap-t method on simulated and real-world data with different levels of random-effect and residual variances, and different numbers of clusters. Close match between the derived expressions and the bootstrap results is observed.

Original languageEnglish
Pages (from-to)88-95
Number of pages8
JournalPattern Recognition Letters
Volume40
Issue number1
DOIs
StatePublished - Apr 15 2014

Bibliographical note

Funding Information:
This study was supported by Grants, CMMI-1100735 and IIS-1218712 , from the National Science Foundation .

Keywords

  • Confidence interval
  • Least squares support vector machine
  • Mixed effect modeling
  • Prediction interval
  • Semiparametric function estimation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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